基于共同邻居有效性的复杂网络链路预测算法（英文）  

Predicting Missing Links of Complex Network via Effective Common Neighbors

作　　者：王凯[1] 刘树新[1] 于洪涛[1] 李星[1] WANG Kai;LIU Shu-xin;YU Hong-tao;LI Xing(National Digital Switching System Engineering and Technological R&D Center Zhengzhou 450002)

机构地区：[1]国家数字交换系统工程技术研究中心,郑州450002

出　　处：《电子科技大学学报》2019年第3期432-437,438,439,共8页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金创新研究群体项目(61521003)

摘　　要：链路预测旨在预测网络中的缺失连边,对于实际网络演化机制的了解具有重要意义。虽然现有研究已经提出了很多相似性指标,但它们都忽视了不同网络结构下共同邻居的有效性,而局部拓扑结构信息尤其是共同邻居结构在计算节点间相似性中发挥重要作用。考虑到共同邻居周围局部拓扑信息,该文提出了一种高效共同邻居指标。该指标首先分析了共同邻居所有连边的有效性,分别从端点两侧量化了节点的有效性;然后,通过分析共同邻居节点拓扑有效性对两侧资源分配过程的影响刻画节点间相似性。15个实际网络数据实验表明,相比现有经典的9种方法,所提方法具有较高的预测精度。Link prediction can predict the missing links of complex networks, which promotes a better understanding of evolution mechanisms in real networks. Many similarity indices have been proposed based on a topology structure for link prediction. Local topological information, especially common neighbors, plays an important role in calculating the similarity between two endpoints. However, plenty of similarity indices ignore the effectiveness of common neighbors under different topology structures. Considering the local topological information around common neighbors, an effective common neighbor index is proposed. Firstly, we analyze the effectiveness of all neighbor links of common neighbors. Then, based on the local topology on both sides of two endpoints around common neighbors, the effectiveness of two sides of common neighbors is quantified separately. Finally, the similarity between two endpoints is described through the effect of common neighbors' effectiveness on bilateral resource allocation process. Empirical study on 15 real networks shows that the index proposed can achieve higher prediction accuracy, compared with 9 mainstream baselines.

关 键 词：复杂网络 共同邻居有效性 链路预测 网络拓扑 相似性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] N94[自动化与计算机技术—计算机科学与技术]